Scott argues that even if AGI requires a new paradigm beyond LLMs, we shouldn't expect significant delays, since Lindy's Law suggests major paradigm shifts could occur within 3-5 years, and new paradigms typically emerge precisely when scaling hits limits. Longer summary

Scott addresses the objection that AGI is far off because LLMs need a 'new paradigm' to reach AGI. He traces the evolutionary tree of AI development from neural networks through transformers to modern LLMs, then applies Lindy's Law to show that even paradigm shifts as major as deep learning or transformers should be expected within 3-5 years at the 25th percentile. He argues this timeline is comparable to LLM-only predictions anyway. Scott also makes a subtler point: new paradigms historically emerge when old ones hit scaling limits, meaning they won't cause delays but rather continue progress from where scaling left off. He concludes that extrapolating from current LLM scaling remains the best forecasting method whether or not LLMs themselves reach AGI. Shorter summary

Scott Alexander argues against Gary Marcus's critique of AI scaling, discussing the potential for future AI capabilities and the nature of human intelligence. Longer summary

Scott Alexander responds to Gary Marcus's critique of AI scaling, arguing that current AI limitations don't necessarily prove statistical AI is a dead end. He discusses the scaling hypothesis, compares AI development to human cognitive development, and suggests that 'world-modeling' may emerge from pattern-matching abilities rather than being a distinct, hard-coded function. Alexander also considers the potential capabilities of future AI systems, even if they don't achieve human-like general intelligence. Shorter summary